相关论文: Root Cause Analysis of Hydrogen Bond Separation in…

Temporal instabilities of a planar liquid jet are studied using direct numerical simulation (DNS) of the incompressible Navier-Stokes equations with level-set (LS) and volume-of-fluid (VoF) surface tracking methods. $\lambda_2$ contours are…

We present a probabilistic approach to water-water hydrogen bonding that allows one to obtain an analytic expression for the number of bonds per water molecule as a function of both its distance to a hydrophobic particle and hydrophobe…

We investigate the microscopic origin of water's anomalies by inspecting the hydrogen bond network (HBN) and the spatial organization of low-density-liquid (LDL) like and high-density-liquid (HDL) like environments. Specifically, we…

The prototypical Hydrogen bond in water dimer and Hydrogen bonds in the protonated water dimer, in other small molecules, in water cyclic clusters, and in ice, covering a wide range of bond strengths, are theoretically investigated by…

An important aspect of astrophysical MHD turbulence research is developing diagnostics to connect simulations with the observable universe. Turbulent systems are by definition structurally complex in all fluid variables (density, velocity,…

Performance monitoring, anomaly detection, and root-cause analysis in complex cyber-physical systems (CPSs) are often highly intractable due to widely diverse operational modes, disparate data types, and complex fault propagation…

Correlation-driven phenomena in molecular periodic systems are challenging to predict computationally not only because such systems are periodically infinite but also because they are typically strongly correlated. Here we generalize the…

Causal discovery aims to infer causal relationships among variables from observational data, typically represented by a directed acyclic graph (DAG). Most existing methods assume independent and identically distributed observations, an…

Heteroscedasticity -- where the variance of a variable changes with other variables -- is pervasive in real data, and elucidating why it arises from the perspective of statistical moments is crucial in scientific knowledge discovery and…

Using modified Arrhenius approximations, the activation energies of water, alcohols, and hexane structure rearrangement reactions responsible for temperature dependences of their dynamic and dielectric characteristics were determined. The…

Ab initio molecular dynamics simulations of liquid water under equilibrium ambient conditions, together with a novel energy decomposition analysis, have recently shown that a substantial fraction of water molecules exhibit a significant…

Four scenarios have been proposed for the low--temperature phase behavior of liquid water, each predicting different thermodynamics. The physical mechanism which leads to each is debated. Moreover, it is still unclear which of the scenarios…

The local intermolecular structure and related dynamics in the liquid cis-trans N-methylformamide mixture at ambient temperature and density conditions have been systematically studied by employing Born-Oppenheimer molecular dynamics…

Halogen bonding has emerged as an important noncovalent interaction in a myriad of applications, including drug design, supramolecular assembly, and catalysis. Current understanding of the halogen bond is informed by electronic structure…

We employ two different atomistic methods to investigate solute-defect interactions in nanosized palladium-hydrogen (Pd-H) systems across multiple time scales. The first method, referred to as Diffusive Molecular Dynamics (DMD), focuses on…

Coarse-graining of fully atomistic molecular dynamics simulations is a long-standing goal in order to allow the description of processes occurring on biologically relevant timescales. For example, the prediction of pathways, rates and…

Causal discovery is a data-driven paradigm for analyzing complex systems, while physics-based models, such as ordinary differential equations (ODEs), provide mechanistic structure for real-world dynamical processes. Integrating these…

We consider the problem of causal discovery (structure learning) from heterogeneous observational data. Most existing methods assume a homogeneous sampling scheme, which leads to misleading conclusions when violated in many applications. To…

The field of hypothesis generation promises to reduce costs in neuroscience by narrowing the range of interventional studies needed to study various phenomena. Existing machine learning methods can generate scientific hypotheses from…

The dynamics of water molecules plays a vital role in understanding water. We combined computer simulation and deep learning to study the dynamics of H-bonds between water molecules. Based on ab initio molecular dynamics simulations and a…